Sensing prior constraints in deep neural networks for solving exploration geophysical problems
One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …
process of analyzing and interpreting geophysical field data that are typically acquired at the …
Applications of deep neural networks in exploration seismology: A technical survey
Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …
controlled (active) source into the ground, and recorded by an array of seismic sensors …
A tutorial of image-domain least-squares reverse time migration through point-spread functions
Least-squares reverse time migration (LSRTM) has shown great potential to improve the
amplitude fidelity and spatial resolution of a reverse time migration (RTM) image. However …
amplitude fidelity and spatial resolution of a reverse time migration (RTM) image. However …
Least-squares reverse time migration via deep learning-based updating operators
Two common issues of least-squares reverse time migration (LSRTM) consist of the many
iterations required to produce substantial subsurface imaging improvements and the …
iterations required to produce substantial subsurface imaging improvements and the …
Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion
Full-waveform inversion (FWI) is a popular technique to obtain high-resolution estimates of
earth model parameters using all information present in seismic data. Thus, it can provide …
earth model parameters using all information present in seismic data. Thus, it can provide …
Machine learning for seismic exploration: Where are we and how far are we from the holy grail?
Machine-learning (ML) applications in seismic exploration are growing faster than
applications in other industry fields, mainly due to the large amount of acquired data for the …
applications in other industry fields, mainly due to the large amount of acquired data for the …
LsmGANs: Image-domain least-squares migration using a new framework of generative adversarial networks
Compared with the traditional adjoint migration, the least-squares migration (LSM) can
effectively mitigate the unbalanced illumination and limited resolution associated with finite …
effectively mitigate the unbalanced illumination and limited resolution associated with finite …
Deep pre-trained FWI: where supervised learning meets the physics-informed neural networks
Full-waveform inversion (FWI) is the current standard method to determine final and detailed
model parameters to be used in the seismic imaging process. However, FWI is an ill-posed …
model parameters to be used in the seismic imaging process. However, FWI is an ill-posed …
Convolutional neural-network-based reverse-time migration with multiple reflections
S Huang, D Trad - Sensors, 2023 - mdpi.com
Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures
and offer high-resolution images of the complex subsurface. Nevertheless, there are some …
and offer high-resolution images of the complex subsurface. Nevertheless, there are some …
Can deep learning compensate for sparse shots in the imaging domain? A potential alternative for reducing the acquisition cost of seismic data
Dense shots can improve the fold of subsurface imaging points, which is essential for the
resolution of imaging results. However, dense shots significantly increase the cost of data …
resolution of imaging results. However, dense shots significantly increase the cost of data …